Process-dependent mechanical and optical properties of nanostructured silicon carbonitride thin films
Bibliographic record
Abstract
Amorphous hydrogenated silicon carbonitride (a-SiCN:H) thin films were grown using electron cyclotron resonance chemical vapour deposition using a mixture of methane, nitrogen, and silane as precursors. The origin of the variation of macroscopic properties such as hardness (H), elastic modulus (E), photoluminescence (PL), and the optical band gap was investigated through their correlation with the microscopic features of a-SiCN:H thin films as a function of the process parameters, including the deposition temperature and methane gas flow rate. From a microstructural perspective, the thin films were investigated using x-ray photoelectron spectroscopy, Rutherford backscattering spectrometry, elastic recoil detection, transmission electron microscopy, and x-ray diffraction. It is verified that an increase of the substrate temperature resulted in the substitution of hydrogen atoms mainly by carbon atoms, causing the density of the silicon carbide-related structures to increase in the amorphous structure of the a-SiCN:H thin films. Hardness and elastic modulus were found to increase with the deposition temperature and decreased with an increase of the methane gas flow during the deposition, resulting in higher carbon content in the films. The observed changes are ascribed to the reduced density of the weak hydrogen terminated bonds and the variation of the relative bond density of Si-C to Si-N bonds. In addition, the thin films were depth profiled using a slow positron beam to investigate the role of vacancies. The observed increase of the positronium formation with increasing deposition temperature was found to correlate with the variation of PL, where an enhancement of the visible emission originating from carbon-related defects was observed. A set of optimized process parameters to fabricate a-SiCN:H thin films with improved visible emission and hardness properties is suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".